prompt-picker

Files (2)

• SKILL.md
• worked-example.md · references/

SKILL.md (first lines)

## Why this skill exists

When buyers use ChatGPT, Gemini, Perplexity, or Claude to research purchases, the
prompts they type are not the same as Google search queries. They are longer, more
contextual, more conversational, and more likely to include role, budget, or use-case
constraints. A brand that monitors only "best [category]" prompts misses the majority
of decision-shaping moments. This skill produces a prompt set that covers discovery,
consideration, evaluation, and action — and separates branded from non-branded prompts
so metrics stay clean.

## Required inputs

Collect these from the user. If any are missing, infer what you can (see the inference
section below) and label those items as assumptions.

| Input                  | Why it matters                                                  |
|------------------------|-----------------------------------------------------------------|
| Brand name             | Anchors all branded and comparison prompts                      |
| Category               | Frames the non-branded discovery and consideration prompts      |
| Top competitors (3–6)  | Powers head-to-head comparison and alternative prompts           |
| Business model         | B2B / B2C / SaaS / ecommerce / services — changes prompt style  |
| ICPs / personas (2–4)  | Drives persona-variant prompts and contextual modifiers          |
| Core use cases / JTBD  | Ensures prompts match real buying reasons, not just features     |
| Differentiators        | Shapes prompts that test whether the brand's positioning lands   |
| Objections / fears     | Generates prompts buyers ask when they are hesitating            |

## Inference when inputs are incomplete

If the user gives only a brand name and category (or just a URL), you can still produce
a useful first draft. Do the following:

1. Search the web for the brand's website, product pages, pricing page, and any review
   sites or comparison articles.
2. From what you find, infer:
   - likely customer segments from pricing tiers and messaging
   - likely competitors from comparison content and review roundups
   - likely objections from FAQ pages, review complaints, and competitor positioning
   - likely use cases from feature pages and case studies
3. Generate the prompt library using these inferences.
4. Clearly label every inference as an assumption at the top of the output so the user
   can correct before finalising.

## Step-by-step method

### Step 1 — Collect and confirm the seed inputs

Present the input table to the user. If they have already supplied everything, confirm
your understanding in a short summary before generating. If gaps exist, state what you
will infer and from where.

### Step 2 — Map the prompt architecture

Organize prompts into five buckets. Within each bucket, use the subcategories below.
Not every subcategory applies to every brand — skip those that do not fit the business
model.

**Bucket 1 — Awareness (non-branded)**
The buyer does not yet know the brand. They are experiencing a problem, exploring a
need, or seeking an outcome. Subcategories:

- Problem-led: "How do I solve [pain]?"
- Objection-led: "Is [fear] a real risk with [category]?"
- Need-discovery: "What kind of tool helps with [job]?"
- Pain-point: "Why is [current approach] so hard?"
- Outcome-seeking: "How can I [desired result] faster?"

**Bucket 2 — Consideration (non-branded)**
The buyer knows the category exists and wants to narrow options. Subcategories:

- Best-of lists: "Best [category] for [persona/use case]"
- Category leaders: "Top [category] companies in [year]"
- Use-case recommendations: "What [category] works best for [specific job]?"
- Feature comparisons: "Which [category] has [key feature]?"
- Budget-aware: "[Category] for [small teams / startups / enterprise]"

**Bucket 3 — Brand evaluation (branded)**
The buyer has heard of the brand and is deciding whether to trust it. These prompts
should be tracked in a separate project so they do not inflate non-branded visibility
scores. Subcategories:

- Head-to-head: "[Brand] vs [Competitor]"
- Worth-it checks: "Is [Brand] worth it for [use case]?"
- Alternative hunts: "[Brand] alternatives for [persona/constraint]"
- Objection probes: "Does [Brand] really [claim/differentiator]?"
- Review / reputation: "[Brand] reviews [year]", "What do people say about [Brand]?"
- Pros/cons: "Pros and cons of [Brand] for [context]"

**Bucket 4 — Purchase / action (branded or non-branded)**
Only include when the business model has a clear purchase or conversion moment.

- Where-to-buy: "Where can I buy [Brand/product]?"
- How-to-start: "How do I get started with [Brand]?"
- Demo / trial: "Does [Brand] have a free trial?"
- Hiring: "Who should I hire for [service category]?"

**Bucket 5 — Persona / context variants**
Take the strongest prompts from Buckets 1–4 and create variants that shift the context.
Vary by:

- Role (CMO vs IC, developer vs product manager, founder vs procurement)
- Company size (startup, mid-market, enterprise)
- Budget sensitivity ("affordable," "cheap," "enterprise-grade")
- Technical maturity ("no-code," "for developers," "easy to set up")
- Industry vertical (healthcare, fintech, ecommerce, etc.)
- Geography or regulation (if relevant)

The point of persona variants is that AI systems return different recommendations
depending on context. A prompt about "best CRM for a 10-person startup" yields
different brands than "best CRM for enterprise sales teams." These variants expose
positioning gaps that a single generic prompt would hide.

### Step 3 — Write each prompt with metadata

For every prompt in the library, attach:

| Field                  | Value                                                           |
|------------------------|-----------------------------------------------------------------|
| Prompt text            | The exact natural-language prompt a user would type into an LLM |
| Funnel stage           | Awareness / Consideration / Evaluation / Purchase               |

From narayan-metaflow/metaflow-marketing-skills, MIT.

More from narayan-metaflow/metaflow-marketing-skills